refactorthis.net - Artificial IntelligenceA blog dedicated to Microsoft technologies, .NET development articles, and tutorialshttp://refactorthis.net/
http://www.rssboard.org/rss-specificationBlogEngine.NET 2.7.0.0en-UShttp://refactorthis.net/opml.axdhttp://www.refactorthis.net/syndication.axdBuddy Jamesrefactorthis.net0.0000000.000000RapidMiner tutorial: How to explore correlations in your data to discover the relevance of attributes<div id="AdnTop"><div class="AdnTopLeft" style="float:left"></div><div class="AdnTopRight" style="float:right"></div><div style="clear:both"></div></div><p><img src="http://refactorthis.net/image.axd?picture=%2f2013%2f06%2frapidminer.jpg" alt="" /></p>
<h1><span style="font-size: 2em;">What is correlation?</span></h1>
<p>From wikipedia</p>
<blockquote>
<p>In statistics, dependence refers to any statistical relationship between two random variables or two sets of data. Correlation refers to any of a broad class of statistical relationships involving dependence.</p>
</blockquote>
<p>In laymans terms, correlation is a relationships between data attributes. &nbsp;For a quick refresher, in data mining, a dataset is made up of different attributes. &nbsp;We use these attributes to classify or predict a label. &nbsp;Some attributes have more "meaning" or influence over the label's value. &nbsp;As you can imagine, if you can determine the influence that specific attributes have over your data, you are in a better position to build a classification model because you will know which attributes you should focus on when building your model. &nbsp;</p>
<p>In this example, I will use the kaggle.com Titanic datamining challenge dataset. &nbsp;This post will not uncover any information that is not readily available in the tutorial posted on kaggle.com.</p>
<p>Here are two screenshots. &nbsp;The first screenshot will show you some statistics about the dataset. &nbsp;The second screenshot will show a sample of the data.</p>
<p>Meta data view of the Titanic data mining challenge Training dataset</p>
<p><img src="http://refactorthis.net/image.axd?picture=%2f2013%2f07%2fTitanic_meta_data_view.png" alt="" /></p>
<p>A data view of the dataset</p>
<p><img src="http://refactorthis.net/image.axd?picture=%2f2013%2f07%2f01+Titanic+data+view.png" alt="" /></p>
<h2>The correlation matrix</h2>
<p>First start by importing the Titanic training dataset into RapidMiner. &nbsp;You can use Read From CSV, Read From Excel, or Read from Database to achieve this step. &nbsp;Next, search for the "Correlation Matrix" operator and drag it onto the process surface. &nbsp;Connect the Titanic training dataset output port to the Correlation Matrix operator's input example port. &nbsp;Your process should look like this.</p>
<p>&nbsp;</p>
<p><img src="http://refactorthis.net/image.axd?picture=%2f2013%2f07%2f01+correlation+matrix.png" alt="" /></p>
<p>Now run the process and observe the output.</p>
<p></p>
<p>You are presented with several different result views. &nbsp;The first view will be the Correlation Matrix Attribute Weights view. &nbsp;The Attribute weights view displays the "weight" of each attribute. &nbsp;The purpose of this tutorial is to explain a different view of the Correlation matrix. &nbsp;Click on the Correlation Matrix view. &nbsp;This is a matrix that shows the Correlation Coefficients which is a measure of the strength of the relationship between our attributes. &nbsp;An easy way to get started with the Correlation matrix is to notice that when an attribute intersects with itself, you have a dark blue cell with the value of 1 which represents the strongest possible value. &nbsp;This is because any attribute matched with itself is a perfect correlation. &nbsp;A correlation coefficient value can be positive or negative. &nbsp;A negative value does not necessarily mean there is less of a relationship between the values represented. &nbsp;The larger the coefficient in either direction represents a strong relationship between those two attributes. &nbsp;If we look at the matrix and follow along the top row (survived) we will see the attributes that have the strongest correlation with the label in which we are trying to predict.</p>
<p><img src="http://refactorthis.net/image.axd?picture=%2f2013%2f07%2f01+correlation+matrix+view.png" alt="" /></p>
<p>Just as the kaggle.com tutorial specifies, the attributes with the strongest correlation with the label (survived) are</p>
<p>sex(0.295), pclass(0.115), and fare(0.66)&nbsp;</p>
<p>Remember that the value as well as the color will help you to visually identify the stronger correlation between attributes.</p>
<p>If you are working with a classification problem, I'm sure you can see how valuable the correlation matrix can be in showing you the relationships between your label and attributes. &nbsp;Such insights let can provide a great start on where to focus your attention when building your classification model.</p>
<p>Thanks for reading and keep your eyes open for my next tutorial!&nbsp;</p><div id="AdnBottom"><div class="AdnBottomLeft" style="float:left"></div><div class="AdnBottomRight" style="float:right"></div><div style="clear:both"></div></div>http://refactorthis.net/post/2013/06/30/RapidMiner-tutorial-How-to-explore-correlations-in-your-data-to-discover-the-relevance-of-attributes.aspx
buddy@refactorthis.comhttp://refactorthis.net/post/2013/06/30/RapidMiner-tutorial-How-to-explore-correlations-in-your-data-to-discover-the-relevance-of-attributes.aspx#commenthttp://refactorthis.net/post.aspx?id=13443c0d-c15c-4080-9a0e-e8c4c6da38e3Sun, 30 Jun 2013 08:41:00 -0300Artificial IntelligenceBig DataData AnalyticsData MiningMachine learningMathPrediction algorithmsRapidMinerStructured learningAdminhttp://refactorthis.net/pingback.axdhttp://refactorthis.net/post.aspx?id=13443c0d-c15c-4080-9a0e-e8c4c6da38e324http://refactorthis.net/trackback.axd?id=13443c0d-c15c-4080-9a0e-e8c4c6da38e3http://refactorthis.net/post/2013/06/30/RapidMiner-tutorial-How-to-explore-correlations-in-your-data-to-discover-the-relevance-of-attributes.aspx#commenthttp://refactorthis.net/syndication.axd?post=13443c0d-c15c-4080-9a0e-e8c4c6da38e3Machine Learning tutorial: How to create a decision tree in RapidMiner using the Titanic passenger data set<div id="AdnTop"><div class="AdnTopLeft" style="float:left"></div><div class="AdnTopRight" style="float:right"></div><div style="clear:both"></div></div><p><a title="RapidMiner machine learning tools" href="http://rapid-i.com"><img src="http://refactorthis.net/image.axd?picture=2013%2f4%2frapidminer.jpg" alt="" /></a>&nbsp;</p>
<p>&nbsp;</p>
<p>Greetings! And welcome to another wam bam, thank you ma'am, mind blowing, flex showing, machine learning tutorial here at refactorthis.net!</p>
<p>This tutorial is based on a machine learning toolkit called RapidMiner by RapidI. &nbsp;RapidMiner is a full featured Java based open source machine learning toolkit with support for all of the popular machine learning algorithms used in data analytics today. &nbsp;The library supports supports the following machine learning algorithms (to name a few):</p>
<ul>
<li>k-NN</li>
<li>Naive Bayes (kernel)</li>
<li>Decision Tree (Weight-based, Multiway)</li>
<li>Decision Stump</li>
<li>Random Tree</li>
<li>Random Forest</li>
<li>Neural Networks</li>
<li>Perception</li>
<li>Linear Regression</li>
<li>Polynomial Regression</li>
<li>Vector Linear Regression</li>
<li>Gaussian Process</li>
<li>Support Vector Machine (Linear, Evolutionary, PSO)</li>
<li>Additive Regression</li>
<li>Relative Regression</li>
<li>k-Means (kernel, fast)</li>
<li>And much much more!!</li>
</ul>
<div>Excited yet? &nbsp;I thought so!</div>
<h2>How to create a decision tree using RapidMiner</h2>
<p>When I first ran across screen shots of RapidMiner online, I thought to myself, "Oh boy.. I wonder how much this is going to cost...". &nbsp;The UI looked so amazing. &nbsp;It's like Visual Studio for Data Mining and Machine learning! &nbsp;Much to my surprise, I found out that the application is open source and free!</p>
<p>Here is a quote from the RapidMiner site:</p>
<blockquote>
<p><span style="color: #402c06; font-family: Arial, Helvetica, sans-serif; font-size: 11px; line-height: 16.5px;">RapidMiner is unquestionably the world-leading open-source system for data mining. It is available as a stand-alone application for data analysis and as a data mining engine for the integration into own products. Thousands of applications of RapidMiner in more than 40 countries give their users a competitive edge.</span></p>
</blockquote>
<p><span style="color: #402c06; font-family: Arial, Helvetica, sans-serif;"><span style="font-size: 11px; line-height: 16.5px;">I've been trying some machine learning "challenges" recently to sharpen my skills as a data scientist, and I decided to use RapidMiner to tackle the <a title="kaggle.com" href="http://kaggle.com">kaggle.com</a> machine learning challenge called <a title="Titanic: Machine learning from disaster" href="http://www.kaggle.com/c/titanic-gettingStarted">"Titanic: Machine Learning from Disaster"</a>&nbsp;. &nbsp;The data set is a CSV file that contains information on many of the passengers of the infamous Titanic voyage. &nbsp;The goal of the challenge is to take one CSV file containing training data (the training data contains all attributes as well as the label Survived) and a testing data file containing only the attributes (no Survived label) and to predict the Survived label of the testing set based on the training set.</span></span></p>
<p><span style="color: #402c06;"><span style="font-size: 11px; line-height: 16.5px;"><strong><em><span style="text-decoration: underline;">Warning: Although I'm not going to provide the complete solution to this challenge, I warn you, if you are working on this challenge, then you should probably stop reading this tutorial. &nbsp;I do provide some insights into the survival data found in the training data set. &nbsp;It's best to try to work the challenge out on your own. &nbsp;After all, we learn by TRYING, FAILING, TRYING AGAIN, THEN SUCCEEDING. &nbsp;I'd also like to say that I'm going to do my very best to go easy on the THEORY of this post.. &nbsp;I know that some of my readers like to get straight to the action :) &nbsp;You have been warned..</span></em></strong></span></span></p>
<p>&nbsp;</p>
<p></p>
<h2><span style="font-size: medium;">Why a decision tree?</span></h2>
<p><span style="font-size: xx-small;">A decision tree model is a great way to visualize a data set to determine which attributes of a data set influenced a particular classification (label). &nbsp;A decision tree looks like a tree with branches, flipped upside down.. &nbsp;Perhaps a (cheesy) image will illustrate..</span></p>
<p><span style="font-size: xx-small;">&nbsp;</span></p>
<p><img src="http://refactorthis.net/image.axd?picture=2013%2f4%2ftwotrees.jpg" alt="" /></p>
<p>After you are finished laughing at my drawing, we may proceed....... &nbsp;OK</p>
<p>In my example, imagine that we have a data set that has data that is related to lifestyle and heart disease. &nbsp;Each row has a person, their sex, age, Smoker (y/n), Diet (good/poor), and a label Risk (Less Risk/More Risk). &nbsp;The data indicates that the biggest influence on Risk turns out to be the Smoker attribute. &nbsp;Smoker becomes the first branch in our tree. &nbsp;For Smokers, the next influencial attribute happens to be Age, however, for non smokers, the data indicates that their diet has a bigger influence on the risk. &nbsp;The tree will branch into two different nodes until the classification os reached or the maximum "depth" that we establish is reached. &nbsp;So as you can see, a decision tree can be a great way to visualize how a decision is derived based on the attributes in your data.</p>
<h2>RapidMiner and data modeling</h2>
<p>Ready to see how easy it is to create a prediction model using RapidMiner? &nbsp;I thought so!</p>
<h3>Create a new process</h3>
<p>When you are working in RapidMiner, your project is known as a process. &nbsp;So we will start by running RapidMiner and creating a new process.</p>
<p>&nbsp;</p>
<p>&nbsp;</p>
<p><img src="http://refactorthis.net/image.axd?picture=%2f2013%2f05%2fStartRapidMiner.png" alt="" /></p>
<p>The version of RapidMiner used in this tutorial is version 5.3. &nbsp;Once the application is open, you will be presented with the following start screen.</p>
<p>&nbsp;From this screen you will click on New Process</p>
<p><img src="http://refactorthis.net/image.axd?picture=%2f2013%2f05%2f02+startscreen.png" alt="" /></p>
<p>&nbsp;You are presented with the main user interface for RapidMiner. &nbsp;One of the most compelling aspects of Rapidminer is it's ease of use and intuitive user interface. &nbsp;The basic flow of this process is as follows:</p>
<ul>
<li>Import your test and training data from CSV files into your RapidMiner repository. &nbsp;This can be found in the repository menu under Import CSV file</li>
<li>Once your data has been imported into your repository, the datasets can be dragged onto your process surface for you to apply operators</li>
<li>You will add your training data to the process</li>
<li>Next, you will add your testing data to the process</li>
<li>Search the operators for Decision Tree and add the operator</li>
<li>In order to use your training data to generate a prediction on your testing data using the Decision Tree model, we will add an "Apply Model" operator to the process. &nbsp;This operator has an input that you will associate with the output model of your Decision Tree operator. &nbsp;There is also an input that takes "unlearned" data from the output of your testing dataset.</li>
<li>You will attach the outputs of Apply Model to the results connectors on the right side of the process surface.</li>
<li>Once you have designed your model, RapidMiner will show you any problems with your process and will offer "Quick fixes" if they exists that you can double click to resolve. &nbsp;</li>
<li>Once all problems have been resolved, you can run your process and you will see the results that you wired up to the results side of the process surface.</li>
<li>Here are screenshots of the entire process for your review</li>
</ul>
<p>&nbsp;Empty Process</p>
<p>&nbsp;</p>
<p><img src="http://refactorthis.net/image.axd?picture=%2f2013%2f05%2f03+newprocess.png" alt="" /></p>
<p>Add the training data from the repository by dragging and dropping the dataset that you imported from your CSV file</p>
<p><img src="http://refactorthis.net/image.axd?picture=%2f2013%2f05%2f04+add+training+data.png" alt="" /></p>
<p>&nbsp;</p>
<p>Repeat the process and add the testing data underneath the training data</p>
<p><img src="http://refactorthis.net/image.axd?picture=%2f2013%2f05%2f05+add+testing+data.png" alt="" /></p>
<p>Now you can search in the operators window for Decision Tree operator. &nbsp;Add it to your process.</p>
<p><img src="http://refactorthis.net/image.axd?picture=%2f2013%2f05%2f06+add+decision+tree.png" alt="" /></p>
<p>The way that you associate the inputs and outputs of operators and data sets is by clicking on the output of one item and connecting it by clicking on the input of another item. &nbsp;Here we are connecting the output of the training dataset to the input of the Decision Tree operator.</p>
<p><img src="http://refactorthis.net/image.axd?picture=%2f2013%2f05%2f07+connect+training+data+to+decision+tree.png" alt="" /></p>
<p>&nbsp;</p>
<p>Next we will add the Apply model operator</p>
<p><img src="http://refactorthis.net/image.axd?picture=%2f2013%2f05%2f08+add+apply+model.png" alt="" /></p>
<p>Then we will create the appropriate connections for the model</p>
<p><img src="http://refactorthis.net/image.axd?picture=%2f2013%2f05%2f09+final+configuration.png" alt="" /></p>
<p>Observe the quick fixes in the problems window at the bottom.. you can double click the quick fixes to resolve the issues.</p>
<p><img src="http://refactorthis.net/image.axd?picture=%2f2013%2f05%2f10+correct+quick+fixes.png" alt="" /></p>
<p>You will be prompted to make a simple decision regarding the problem that was detected. &nbsp;Once you resolve one problem, other problems may appear. &nbsp;be sure to resolve all problems so that you can run your process.</p>
<p><img src="http://refactorthis.net/image.axd?picture=%2f2013%2f05%2f11+double+click+quick+fix.png" alt="" /></p>
<p>Here is the process after resolving all problems.</p>
<p><img src="http://refactorthis.net/image.axd?picture=%2f2013%2f05%2f12+final+process.png" alt="" /></p>
<p>&nbsp;</p>
<p>Next, I select the decision tree operator and I adjust the following parameters:</p>
<p>Maximum Depth: change from 20 to 5.</p>
<p>check both boxes to make sure that the tree is not "pruned".</p>
<p>Once this has been done, you can Run your process and observe the results. &nbsp;Since we connected both the model as well as the labeled result to the output connectors of the process, we are presented with a visual display of our Decision Tree (model) as well as the Test data set with the prediction applied.</p>
<p>(Decision Tree Model)</p>
<p><img src="http://refactorthis.net/image.axd?picture=%2f2013%2f05%2fresults+1.png" alt="" /></p>
<p>&nbsp;</p>
<p>(The example test result set with the predictions applied)</p>
<p><img src="http://refactorthis.net/image.axd?picture=%2f2013%2f05%2fresults+2.png" alt="" /></p>
<p>&nbsp;</p>
<p>As you can see, RapidMiner makes complex data analysis and machine learning tasks extremely easy with very little effort.</p>
<p>This concludes my tutorial on creating Decision Trees in RapidMiner.</p>
<p>Until next time,</p>
<p>&nbsp;</p>
<p>Buddy James</p>
<p>&nbsp;</p><div id="AdnBottom"><div class="AdnBottomLeft" style="float:left"></div><div class="AdnBottomRight" style="float:right"></div><div style="clear:both"></div></div>http://refactorthis.net/post/2013/04/10/Machine-Learning-tutorial-How-to-create-a-decision-tree-in-RapidMiner-using-the-Titanic-passenger-data-set.aspx
buddy@refactorthis.comhttp://refactorthis.net/post/2013/04/10/Machine-Learning-tutorial-How-to-create-a-decision-tree-in-RapidMiner-using-the-Titanic-passenger-data-set.aspx#commenthttp://refactorthis.net/post.aspx?id=587932de-4912-4d21-9e3c-d81ee0d8789fWed, 10 Apr 2013 21:06:00 -0300Artificial IntelligenceBig DataDecision TreesDesign PatternsMachine learningPrediction algorithmsProduct ReviewRapidMinerReviewsStructured learningAdminhttp://refactorthis.net/pingback.axdhttp://refactorthis.net/post.aspx?id=587932de-4912-4d21-9e3c-d81ee0d8789f31http://refactorthis.net/trackback.axd?id=587932de-4912-4d21-9e3c-d81ee0d8789fhttp://refactorthis.net/post/2013/04/10/Machine-Learning-tutorial-How-to-create-a-decision-tree-in-RapidMiner-using-the-Titanic-passenger-data-set.aspx#commenthttp://refactorthis.net/syndication.axd?post=587932de-4912-4d21-9e3c-d81ee0d8789fnuml - a machine learning library for .NET developers<div id="AdnTop"><div class="AdnTopLeft" style="float:left"></div><div class="AdnTopRight" style="float:right"></div><div style="clear:both"></div></div><p><a title="numl machine learning for .NET developers" href="http://numl.net"><img src="http://refactorthis.net/image.axd?picture=2013%2f3%2fnumllogo.jpg" alt="" /></a></p>
<p>In one of my previous posts called <a title="Machine learning resources for .NET developers" href="http://www.refactorthis.net/post/2013/02/23/Machine-learning-resources-for-NET-developers.aspx">Machine learning resources for .NET developers</a>, I introduced a machine learning library called numl.net. &nbsp;numl.net is a machine learning library for .NET created by Seth Juarez. &nbsp;You can <a title="Official numl.net site" href="http://numl.net">find the library here</a> and <a title="Seth Juarez blog" href="http://sethjuarez.com/">Seth's blog here</a>. &nbsp;When I began researching the library, I learned quickly that one of Seth's goals in writing numl.net was to abstract away the complexities that stops many software developers from trying their hand at machine learning. &nbsp;I must say that in my opinion, he has done a wonderful job in accomplishing this goal!</p>
<h2>Tutorial</h2>
<p>I've decided to throw together a small tutorial to show you just how easy it is to use numl.net to perform predictions. &nbsp;This tutorial will use structured learning by way of a decision tree to perform predictions. &nbsp;I will use the infamous <a title="Iris flower data set" href="http://en.wikipedia.org/wiki/Iris_flower_data_set">Iris Data set</a> which contains data 3 different types of Iris flowers and the data that defines them. &nbsp;Before we get into code, let's look at some basic terminology first.</p>
<p>With numl.net you create a POCO (plain old CLR object) to use for training as well as predictions. &nbsp;There will be properties that you will specify known values (features) so that you can predict the value of an unknown property value (label). &nbsp;numl.net makes identifying features and labels easy, you simply mark your properties with the [Feature] attribute or the [Label] attribute (there is also a [StringLabel] attribute as well). &nbsp;Here is an example of the Iris class that we will use in this tutorial.</p>
<pre class="brush: c-sharp;">using numl.Model;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
namespace NumlDemo
{
/// &lt;summary&gt;
/// Represents an Iris in the infamous Iris classification dataset (Fisher, 1936)
/// Each feature property will be used for training as well as prediction. The label
/// property is the value to be predicted. In this case, it's which type of Iris we are dealing with.
/// &lt;/summary&gt;
public class Iris
{
//Length in centimeters
[Feature]
public double SepalLength { get; set; }
//Width in centimeters
[Feature]
public double SepalWidth { get; set; }
//Length in centimeters
[Feature]
public double PetalLength { get; set; }
//Width in centimeters
[Feature]
public double PetalWidth { get; set; }
//-- Iris Setosa
//-- Iris Versicolour
//-- Iris Virginica
public enum IrisTypes
{
IrisSetosa,
IrisVersicolour,
IrisVirginica
}
[Label]
public IrisTypes IrisClass { get; set; } //This is the label or value that we wish to predict based on the supplied features
}
}</pre>
<p class="brush: c-sharp;">As you can see, we have a simple POCO Iris class, which defines four features and one label. &nbsp;The <a title="Iris Data set" href="http://archive.ics.uci.edu/ml/datasets/Iris">Iris training data can be found here</a>&nbsp;. &nbsp;Here is an example of the data found in the file.</p>
<p class="brush: c-sharp;">&nbsp;</p>
<p class="brush: c-sharp;">5.1,3.5,1.4,0.2,Iris-setosa</p>
<p class="brush: c-sharp;">6.3,2.5,4.9,1.5,Iris-versicolor</p>
<div>6.0,3.0,4.8,1.8,Iris-virginica</div>
<div>&nbsp;</div>
<p></p>
<div>&nbsp;</div>
<div>The first four values are doubles which represent the features Sepal Length, Sepal Width, Petal Length, Petal Width. &nbsp;The final value is an enum that represents the label that we will predict which is the class of Iris.</div>
<div>&nbsp;</div>
<div>We have the Iris class, so now we need a method to parse the training data file and generate a static List&lt;Iris&gt; collection. &nbsp;Here is the code:</div>
<div>&nbsp;</div>
<div>
<pre class="brush: c-sharp;">using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
namespace NumlDemo
{
/// &lt;summary&gt;
/// Provides the services to parse the training data files
/// &lt;/summary&gt;
public static class IrisDataParserService
{
//provides the training data to create the predictive model
public static List&lt;Iris&gt; TrainingIrisData { get; set; }
/// &lt;summary&gt;
/// Reads the trainingDataFile and populates the TrainingIrisData list
/// &lt;/summary&gt;
/// &lt;param name="trainingDataFile"&gt;File full of Iris data&lt;/param&gt;
/// &lt;returns&gt;&lt;/returns&gt;
public static void LoadIrisTrainingData(string trainingDataFile)
{
//if we don't have a training data file
if (string.IsNullOrEmpty(trainingDataFile))
throw new ArgumentNullException("trainingDataFile");
//if the file doesn't exist on the file system
if (!File.Exists(trainingDataFile))
throw new FileNotFoundException();
if (TrainingIrisData == null)
//initialize the return training data set
TrainingIrisData = new List&lt;Iris&gt;();
//read the entire file contents into a string
using (var fileReader = new StreamReader(new FileStream(trainingDataFile, FileMode.Open)))
{
string fileLineContents;
while ((fileLineContents = fileReader.ReadLine()) != null)
{
//split the current line into an array of values
var irisValues = fileLineContents.Split(',');
double sepalLength = 0.0;
double sepalWidth = 0.0;
double petalLength = 0.0;
double petalWidth = 0.0;
if (irisValues.Length == 5)
{
Iris currentIris = new Iris();
double.TryParse(irisValues[0], out sepalLength);
currentIris.SepalLength = sepalLength;
double.TryParse(irisValues[1], out sepalWidth);
currentIris.SepalWidth = sepalWidth;
double.TryParse(irisValues[2], out petalLength);
currentIris.PetalLength = petalLength;
double.TryParse(irisValues[3], out petalWidth);
currentIris.PetalWidth = petalWidth;
if (irisValues[4] == "Iris-setosa")
currentIris.IrisClass = Iris.IrisTypes.IrisSetosa;
else if (irisValues[4] == "Iris-versicolor")
currentIris.IrisClass = Iris.IrisTypes.IrisVersicolour;
else
currentIris.IrisClass = Iris.IrisTypes.IrisVirginica;
IrisDataParserService.TrainingIrisData.Add(currentIris);
}
}
}
}
}
}</pre>
</div>
<div>This code is pretty standard. &nbsp;We simply read each line in the file, split the values out into an array, and populate a List&lt;Iris&gt; collection of Iris objects based on the data found in the file.</div>
<div>&nbsp;</div>
<h2>Now the magic</h2>
<p>Using the numl.net library, we need only use three classes to perform a prediction based on the Iris data set. &nbsp;We start with a Descriptor, which identifies the class in which we will learn and predict. &nbsp;Next, we will instantiate a DecisionTreeGenerator, passing the descriptor to the constructor. &nbsp;Finally, we will create our prediction model by calling the Generate method of the DecisionTreeGenerator, passing the training data (IEnumerable&lt;Iris&gt;) to the Generate method. &nbsp;The generate method will provide us with a model in which we can perform our prediction.</p>
<p>Here is the code:</p>
<pre class="brush: c-sharp;">using numl;
using numl.Model;
using numl.Supervised;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
namespace NumlDemo
{
class Program
{
public static void Main(string[] args)
{
//get the descriptor that describes the features and label from the Iris training objects
var irisDescriptor = Descriptor.Create&lt;Iris&gt;();
//create a decision tree generator and teach it about the Iris descriptor
var decisionTreeGenerator = new DecisionTreeGenerator(irisDescriptor);
//load the training data
IrisDataParserService.LoadIrisTrainingData(@"D:\Development\machinelearning\Iris Dataset\bezdekIris.data");
//create a model based on our training data using the decision tree generator
var decisionTreeModel = decisionTreeGenerator.Generate(IrisDataParserService.TrainingIrisData);
//create an iris that should be an Iris Setosa
var irisSetosa = new Iris
{
SepalLength = 5.1,
SepalWidth = 3.5,
PetalLength = 1.4,
PetalWidth = 0.2
};
//create an iris that should be an Iris Versicolor
var irisVersiColor = new Iris
{
SepalLength = 6.1,
SepalWidth = 2.8,
PetalLength = 4.0,
PetalWidth = 1.3
};
//create an iris that should be an Iris Virginica
var irisVirginica = new Iris
{
SepalLength = 7.7,
SepalWidth = 2.8,
PetalLength = 6.7,
PetalWidth = 2.0
};
var irisSetosaClass = decisionTreeModel.Predict&lt;Iris&gt;(irisSetosa);
var irisVersiColorClass = decisionTreeModel.Predict&lt;Iris&gt;(irisVersiColor);
var irisVirginicaClass = decisionTreeModel.Predict&lt;Iris&gt;(irisVirginica);
Console.WriteLine("The Iris Setosa was predicted as {0}",
irisSetosaClass.IrisClass.ToString());
Console.WriteLine("The Iris Versicolor was predicted as {0}",
irisVersiColorClass.IrisClass.ToString());
Console.WriteLine("The Iris Virginica was predicted as {0}",
irisVirginicaClass.IrisClass.ToString());
Console.ReadKey();
}
}
}</pre>
<p class="brush: c-sharp;">And that's all there is to it. &nbsp;As you can see, you can use the prediction model accurately and there's no math, only simple abstractions.</p>
<p class="brush: c-sharp;">I hope this has peaked your interest in the numl.net library for machine learning in .NET. &nbsp;</p>
<p class="brush: c-sharp;">Feel free to post any questions or opinions.</p>
<p class="brush: c-sharp;">Thanks for reading!</p>
<p class="brush: c-sharp;">Buddy James</p>
<p>&nbsp;</p><div id="AdnBottom"><div class="AdnBottomLeft" style="float:left"></div><div class="AdnBottomRight" style="float:right"></div><div style="clear:both"></div></div>http://refactorthis.net/post/2013/03/26/numl-a-machine-learning-library-for-NET-developers.aspx
buddy@refactorthis.comhttp://refactorthis.net/post/2013/03/26/numl-a-machine-learning-library-for-NET-developers.aspx#commenthttp://refactorthis.net/post.aspx?id=566b12aa-0ea2-4538-9d3c-fb4e9e12b46fTue, 26 Mar 2013 23:57:00 -0300.NETArtificial IntelligenceBig Datac#Decision TreesDevelopmentMachine learningnuml.netPrediction algorithmsSoftware DesignStructured learningAdminhttp://refactorthis.net/pingback.axdhttp://refactorthis.net/post.aspx?id=566b12aa-0ea2-4538-9d3c-fb4e9e12b46f107http://refactorthis.net/trackback.axd?id=566b12aa-0ea2-4538-9d3c-fb4e9e12b46fhttp://refactorthis.net/post/2013/03/26/numl-a-machine-learning-library-for-NET-developers.aspx#commenthttp://refactorthis.net/syndication.axd?post=566b12aa-0ea2-4538-9d3c-fb4e9e12b46fAssistant Professor receives $518,434 to apply Machine Learning to network analysis<div id="AdnTop"><div class="AdnTopLeft" style="float:left"></div><div class="AdnTopRight" style="float:right"></div><div style="clear:both"></div></div><p>The University of Illinois at Urbana-Champaign - College of Engineering has awarded $518,434 to Assistant Professor Maxim Raginsky to use to apply Machine Learning techniques to network analysis to try and discover how to make networks more efficient.</p>
<p>From the article</p>
<p><a href="http://csl.illinois.edu/news/raginsky-receives-career-award-apply-information-theory-machine-learning-problems">http://csl.illinois.edu/news/raginsky-receives-career-award-apply-information-theory-machine-learning-problems</a></p>
<blockquote>
<p>&ldquo;The overall design objective is to make sure that the network resources are allocated in a smart way, and each user receives only the data they need without significant waste of bandwidth or power,&rdquo;&nbsp; said Raginsky, a member of Illinois' electrical and computer engineering faculty.</p>
<p>Raginsky uses ecological monitoring as an example. If someone is tracking a rare bird species in a specific habitat and wants to record how many of these birds fly in and out of the area, it would be a waste of resources to continuously stream video if what the person really wants is just the arrivals and departures of the birds. A big part of the problem is learning to detect events of interest and to reliably communicate only the data describing these events.</p>
<p>&ldquo;So I want to make sure that only the relevant information gets to those who need it, despite the fact that everyone is using the same network and the kinds of information that are relevant to one user are different than the kinds of information that are relevant to somebody else,&rdquo; Raginsky said.</p>
<p>These problems are messy and complex, and there is no hope to come up with an accurate model for all kinds of data being transmitted and received over networks because of the increasing size and complexity of both the networks and the data, Raginsky said. Machine learning offers a variety of tools for extracting predictively relevant information from observations, but to date most of the research on machine learning has not focused on the network aspect and all the resource constraints that it imposes.</p>
<p>This project will systematically explore what is and is not possible in these types of large networks with multiple learning agents, specifically identifying the effect of bandwidth limitations, losses, delays and lack of central coordination on the performance of statistical learning algorithms, thus helping develop efficient and robust coding/decoding schemes.</p>
</blockquote>
<p></p>
<p>The NSF CAREER Award is awarded by the National Science Foundation specifically to &ldquo;junior faculty members who demonstrate their roles through outstanding research and education,&rdquo; according to NSF&rsquo;s website.</p>
<blockquote>
<p>Raginsky said that because these awards are for 5-year projects, the proposals take a lot of time and effort. &ldquo;You propose to research something you&rsquo;re really passionate about, and presumably you want to work on this topic even if it did not get funded,&rdquo; Raginsky said. &ldquo;So, when I heard about my proposal being recommended for funding, of course it was a relief. I will have a good time working on this problem.&rdquo;</p>
</blockquote>
<p>Raginsky is a member of the Decision and Control group at CSL.</p>
<p>I think that this is a wonderful problem domain in which Machine learning can prove useful.&nbsp; Machine learning is a powerful set&nbsp;of&nbsp;technologies, and we have yet to even scratch the surface of what it can do&nbsp;for&nbsp;human kind.&nbsp; This goes to show you that there are other great uses besides targeted advertising systems, though that is where&nbsp;most of the jobs are at the moment.&nbsp;</p>
<p>Do you have ay ideas as to some practical applications of Machine learning that have yet to be tested?</p>
<p>Please share by leaving a comment.</p>
<p>&nbsp;</p><div id="AdnBottom"><div class="AdnBottomLeft" style="float:left"></div><div class="AdnBottomRight" style="float:right"></div><div style="clear:both"></div></div>http://refactorthis.net/post/2013/03/04/Assistant-Professor-receives-$518434-to-apply-Machine-Learning-to-network-analysis.aspx
buddy@refactorthis.comhttp://refactorthis.net/post/2013/03/04/Assistant-Professor-receives-$518434-to-apply-Machine-Learning-to-network-analysis.aspx#commenthttp://refactorthis.net/post.aspx?id=0a5a2f68-9187-4950-ae5e-dc9027f9bee2Mon, 04 Mar 2013 18:18:00 -0300Artificial IntelligenceBig DataMachine learningMathNeural NetworksPrediction algorithmsSimulated EvolutionTech NewsAdminhttp://refactorthis.net/pingback.axdhttp://refactorthis.net/post.aspx?id=0a5a2f68-9187-4950-ae5e-dc9027f9bee264http://refactorthis.net/trackback.axd?id=0a5a2f68-9187-4950-ae5e-dc9027f9bee2http://refactorthis.net/post/2013/03/04/Assistant-Professor-receives-$518434-to-apply-Machine-Learning-to-network-analysis.aspx#commenthttp://refactorthis.net/syndication.axd?post=0a5a2f68-9187-4950-ae5e-dc9027f9bee2Machine learning resources for .NET developers<div id="AdnTop"><div class="AdnTopLeft" style="float:left"></div><div class="AdnTopRight" style="float:right"></div><div style="clear:both"></div></div><p><img src="http://refactorthis.net/image.axd?picture=2013%2f2%2fmachinelearningdotnet.jpg" alt="Machine learning for .NET" width="300px" height="333px" /></p>
<p>Greetings friends and welcome to this article on Machine learning libraries for .NET developers.&nbsp; Machine learning is a hot topic right now and for good reason.&nbsp; Personally, I haven't been so excited about a technology since my computer used my 2800 baud modem to dial into a BBS over 17 years ago.&nbsp; The thought that my computer could&nbsp;communicate with another computer was so fascinating to me.&nbsp; That moment was the very moment that would forever change my life.&nbsp; I learned a lot about DOS by writing batch scripts and running other programs that allowed me to visit and then run a BBS system.&nbsp; It eventually lead me to QBasic.&nbsp; I wanted to learn to write BBS door games and QBasic was included as a part of a standard DOS installation back then.</p>
<p>Fast forward 17 years and I'm still in love with computers, programming, and the concept of communication between machines.&nbsp;&nbsp;The magic never disappeared.&nbsp; So when i first learned about the concept of Machine learning, I felt like that 13 year old kid again.&nbsp; The idea that a machine can learn to do things that it has not been programmed to do is now a passion of mine.&nbsp; The concepts of Machine learning have an extreme learning curve, however, I believe that we as humans can do anything that we put our mind to.&nbsp; So I began looking around for tutorials on machine learning.&nbsp; I found many great tutorials and books, however, most of them involved using python.&nbsp; I have nothing against python.&nbsp; As a matter of fact, I find it ironic that I started with BASIC and now in this moment of "rebirth" I'm beginning to use python which looks a lot like BASIC in many ways.&nbsp; The fact of the matter remains, I'm a .NET developer.&nbsp; I've spent the last 9 years in the .NET framework and I love the technology.&nbsp; C# is an awesome programming language and it's hard to imagine life without Visual Studio.&nbsp; What can I say, the IDE has spoiled me.</p>
<p>While I scoured the internet looking for tutorials related to Machine learning resources for .NET developers, I wished that there was a one resource that would assist me in my search for resources to help me achieve my goal.</p>
<p>Well that's what this article is all about.&nbsp; In this article, I will introduce you to some .NET libraries that will assist you in your quest to learn about Machine learning.</p>
<p></p>
<h2>NND Neural Network Designer by Bragisoft</h2>
<p><a title="Neural Network Designer" href="http://www.bragisoft.com">The Neural Network Designer project (NND)</a> is a DBMS management system for neural networks that was created by Jan&nbsp;Bogaerts. &nbsp;The designer application is developed using WPF, and is a user interface which allows you to design your neural network, query the network, create and configure chat bots that are capable of asking questions and learning from your feed back. &nbsp;The chat bots can even scrape the internet for information to return in their output as well as to use for learning. &nbsp;The project includes a custom language syntax called NNL (neural network language) that you can use in configuring your machine learning project. &nbsp;The source code is designed so that the libraries can be used in your own custom applications so you don't have to start from scratch with such a complex set of technologies.&nbsp;&nbsp;The project is actually an open source project in which I am a&nbsp;part of.&nbsp;&nbsp;Some of the possibilities offered by this awesome project include predictions, image and pattern recognition, value inspection, memory profiling and much more. &nbsp;Stop by the<a title="Neural Network Designer" href="http://www.bragisoft.com"> Bragisoft NND website and download the application to give it a try</a>.&nbsp;</p>
<h2>&nbsp;Screen shots of the neural network designer by Bragisoft</h2>
<h3>A DBMS for neural networks</h3>
<p><img title="A DBMS for neural networks" src="http://bragisoft.com/wp-content/uploads/et_temp/QueryWordsDict-32064_414x269.jpg" alt="A DBMS for neural networks" /></p>
<p>&nbsp;</p>
<h3>Mind map rand forrest</h3>
<p><img title="Machine learning" src="http://bragisoft.com/wp-content/uploads/et_temp/mindmap-random-forest-107154_439x324.jpg" alt="Machine learning" /></p>
<h3>The chat bot designer and other tools</h3>
<p><img title="GUIs and debuggers" src="http://bragisoft.com/wp-content/uploads/et_temp/collage1-136157_439x324.jpg" alt="GUIs and debuggers" /></p>
<h2>Accord.net</h2>
<p>Here is a description from the <a title="Accord.NET" href="http://code.google.com/p/accord/">Accord.NET&nbsp;project website</a>&nbsp;</p>
<blockquote>
<p><strong>Accord.NET</strong> is a framework for scientific computing in .NET. The framework builds upon <em>AForge.NET</em>, an also popular framework for image processing, supplying new tools and libraries. Those libraries encompass a wide range of scientific computing applications, such as statistical data processing, machine learning, pattern recognition, including but not limited to, computer vision and computer audition. The framework offers a large number of probability distributions, hypothesis tests, kernel functions and support for most popular performance measurements techniques.</p>
</blockquote>
<p>&nbsp;The most impressive parts of this library has got to be the documentation and sample applications that are distributed with the project.&nbsp; This makes the library easy to get started using.&nbsp; I also like the ability to perform operations like Audio processing (beat detection and more), Video processing (easy integration with your web cam, vision capabilities and object recognition).&nbsp; This is an excellent place to start with approaching Machine learning with the .NET framework.&nbsp; Here are a two videos that should whet your appetite.</p>
<p>Hand writing recognition with Accord.NET</p>
<p><iframe src="http://www.youtube.com/embed/BesKtH4Qln8" frameborder="0" width="560" height="315"></iframe></p>
<p>&nbsp;</p>
<p>Here is an example of head tracking with Accord.NET (super cool)</p>
<p><iframe src="http://www.youtube.com/embed/tqAfqJsW2Wo" frameborder="0" width="560" height="315"></iframe></p>
<p>&nbsp;</p>
<h2>AIMLBot Program# AILM Chat bot library</h2>
<p>AIMLBot (Program#) is a small, fast, standards-compliant yet easily customizable implementation of an <a href="http://www.alicebot.org/">AIML</a> (Artificial Intelligence Markup Language) based chatter bot in C#. AIMLBot has been tested on both Microsoft's runtime environment and Mono. Put simply, it will allow you to chat (by entering text) with your computer using natural language.&nbsp; <a title="Program#" href="http://aimlbot.sourceforge.net/">The project is located here.</a></p>
<h2>Math.NET</h2>
<p>Machine learning algorithms are extremely math heavy.&nbsp; <a href="http://www.mathdotnet.com/">Math.NET is a library </a>&nbsp;that can assist with the math that is required to solve machine learning related problems.</p>
<blockquote>
<p>Math.NET Numerics aims to provide methods and algorithms for numerical computations in science, engineering and every day use. Covered topics include special functions, linear algebra, probability models, random numbers, interpolation, integral transforms and more.</p>
</blockquote>
<h2>DotNumerics</h2>
<blockquote>
<p>DotNumerics is a website dedicated to numerical computing for .NET. DotNumerics includes a <a href="http://www.dotnumerics.com/NumericalLibraries/Default.aspx">Numerical Library for .NET.</a> The library is written in pure C# and has more than 100,000 lines of code with the most advanced algorithms for Linear Algebra, Differential Equations and Optimization problems. The Linear Algebra library includes <a href="http://www.dotnumerics.com/NumericalLibraries/LinearAlgebra/CSLapack/Default.aspx">CSLapack</a>, <a href="http://www.dotnumerics.com/NumericalLibraries/LinearAlgebra/CSBlas/Default.aspx">CSBlas</a> and CSEispack, these libraries are the translation from Fortran to C# of LAPACK, BLAS and EISPACK, respectively.</p>
</blockquote>
<p><a title="http://www.dotnumerics.com/" href="http://www.dotnumerics.com/">You can find the library here.</a>&nbsp;</p>
<h3>ALGLIB</h3>
<p>ALGLIB is a cross-platform numerical analysis and data processing library. It supports several programming languages (C++, C#, Pascal, VBA) and several operating systems (Windows, Linux, Solaris). ALGLIB features include:</p>
<h3>Accessing &lsquo;R&rsquo; from C#&ndash;Lessons learned</h3>
<p><a title="http://www.billbak.com/2010/11/accessing-r-from-clessons-learned/" href="http://www.billbak.com/2010/11/accessing-r-from-clessons-learned/">Here are instructions to use the R statistical framework from within c#</a></p>
<h3>ILNumerics</h3>
<p><a title="ILNumerics" href="http://www.ilnumerics.net">You can check out the library at http://www.ilnumerics.net</a></p>
<h3>NuML.net http://numl.net</h3>
<p>A nice <a title="http://numl.net" href="http://numl.net">site about the basics of machine learning in c# by Seth Juarez</a>&nbsp;. NuML.NET is a machine learning library for .NET developers written by Seth Juarez. &nbsp;I've recently tried this library and I'm impressed! &nbsp;Seth has stated publicly that his intention behind the numl.net library is to abstract the scary math away from machine learning to provide tools that are more approachable by software developers and boy did he deliver! &nbsp;I've been working with this library for a little more than an hour and I've written a prediction app in c#. &nbsp;You can <a title="numl.net" href="https://github.com/sethjuarez/numl">find his numl.net library source on github.</a></p>
<h3>Encog Machine Learning Framework</h3>
<p>Here is what the official Heaton Research website has to say about Encog:</p>
<blockquote>
<p style="font-size: 13px; line-height: 20.15999984741211px;"><span style="font-size: x-small;">Encog is an advanced machine learning framework that supports a variety of advanced algorithms, as well as support classes to normalize and process data. Machine learning algorithms such as Support Vector Machines, Artificial Neural Networks, Genetic Programming, Bayesian Networks, Hidden Markov Models and Genetic Algorithms are supported. Most Encog training algoritms are multi-threaded and scale well to multicore hardware. Encog can also make use of a GPU to further speed processing time. A GUI based workbench is also provided to help model and train machine learning algorithms. Encog has been in active development since 2008.</span></p>
<p style="font-size: 13px; line-height: 20.15999984741211px;"><span style="font-size: x-small;">Encog is available for Java, .Net and C/C++.</span></p>
</blockquote>
<p>Jeff Heaton knows a great deal about machine learning algorithms and he's created a wonderful library called Encog. &nbsp;I was able to write a neural network application that solved the classic XOR problem in 20 minutes after installing the library. &nbsp;What really amazes me is that he has an Encog Library for JavaScript which includes live samples on his website of Javascript + encog solving problems like the Traveling Salesman Problem and Conway's game of life, all in a browser! &nbsp;This library can even use your GPU for the heavy lifting if that's your choice. &nbsp;I would highly recommend that you at least check out his site and download the library to look at the examples. &nbsp;You can find the Encog library <a title="Encog Machine Learning Library" href="http://www.heatonresearch.com/encog">here</a>.&nbsp;<a title="numl.net" href="https://github.com/sethjuarez/numl"><br /></a></p>
<p>&nbsp;</p>
<h3>Conclusion</h3>
<p>This concludes my article on Machine learning resources for the .NET developer.&nbsp; If you have any suggestions regarding a project that you know of or you are working on related to Machine learning in .NET, please don't hesitate to leave a comment and I will update the article to mention the project.&nbsp; This article has shown that we as .NET developers have many resources available to us to use to implement Machine learning based solutions.&nbsp; I appreciate your time in reading this article and I hope you found it useful.&nbsp; Please subscribe to my RSS feed.&nbsp; Until next time..</p>
<p>Buddy James</p><div id="AdnBottom"><div class="AdnBottomLeft" style="float:left"></div><div class="AdnBottomRight" style="float:right"></div><div style="clear:both"></div></div>http://refactorthis.net/post/2013/02/23/Machine-learning-resources-for-NET-developers.aspx
buddy@refactorthis.comhttp://refactorthis.net/post/2013/02/23/Machine-learning-resources-for-NET-developers.aspx#commenthttp://refactorthis.net/post.aspx?id=40d0ef0d-fdcd-4a09-a3b2-3be1d9c7039eSat, 23 Feb 2013 22:40:00 -0300.NETArtificial IntelligenceBragisoftc#Data AnalyticsData MiningDecision TreesMachine learningMathNeural NetworksPrediction algorithmsProduct ReviewSimulated EvolutionSoftware DesignAdminhttp://refactorthis.net/pingback.axdhttp://refactorthis.net/post.aspx?id=40d0ef0d-fdcd-4a09-a3b2-3be1d9c7039e118http://refactorthis.net/trackback.axd?id=40d0ef0d-fdcd-4a09-a3b2-3be1d9c7039ehttp://refactorthis.net/post/2013/02/23/Machine-learning-resources-for-NET-developers.aspx#commenthttp://refactorthis.net/syndication.axd?post=40d0ef0d-fdcd-4a09-a3b2-3be1d9c7039eMachine Learning: 5 examples of what it is and why you should care<div id="AdnTop"><div class="AdnTopLeft" style="float:left"></div><div class="AdnTopRight" style="float:right"></div><div style="clear:both"></div></div><p>Machine learning examples to make you think</p>
<table id="machineLearningContent">
<tbody>
<tr>
<td valign="top">
<p><img src="http://refactorthis.net/image.axd?picture=2013%2f2%2fjohnny5.jpg" alt="Johnny #5 loves machine learning" /></p>
</td>
<td valign="top">
<p>Hello folks, and welcome to another awe inspiring article from refactorthis.net . &nbsp;This article is one that I'm very excited to present. &nbsp;I'm sure you've guessed by now that the topic of this post is Machine learning. &nbsp; If you don't know what machine learning is or don't care, I ask you to take a look at the fascinating examples that I've presented in this article. &nbsp;You just may get inspired. &nbsp;</p>
<h2>Background</h2>
<p>I'm a .NET developer and I have experience working in a myriad of different business domains. &nbsp;My love affair with machine learning was brought about while I was working for an e-commerce website. &nbsp;My boss had asked me to look into a new API that Google had released in beta at the time that would allow you to provide data about your customers and it would suggest products based on their shopping data. &nbsp;That API is called Google prediction and you can read about the <a title="Google prediction API" href="https://developers.google.com/prediction/">Google prediction API here.</a>&nbsp;</p>
<p>It's been a couple of years since I was introduced to this technology and since that time I've contemplated how machine learning algorithms work and what possibilities they could unlock with the right amount of data and creativity.</p>
<p>Since then I've done a lot of reading, and planning on ways that I can collect data to use in my journey to learn as much as I can about this new frontier that we as software developers are facing. &nbsp;I was fortunate enough become <a title="bagisoft's first team member" href="http://bragisoft.com/2013/01/first-team-member/">the first accepted team member of the open source project called NND, or Neural Network Designer by Bragisoft.</a>&nbsp; Check back soon for an article dedicated to this wonderful open source project on machine learning and neural networks.</p>
<h2>Proceed with caution</h2>
<p>Let it be known, however, that machine learning algorithms are not for the faint of heart. &nbsp;This is a very complex array of concepts and I don't plan to &nbsp;try to explain them in this article. &nbsp;What I will do is give a brief, simple introduction to a few of the prevalent topics that one would need to research in order to implement machine learning algorithms. &nbsp;The main purpose of this article is to provide some wonderful youtube videos that provide insight into the possibilities of machine learning and it's practical applications. &nbsp;Some of the videos just may blow your mind!</p>
<p>So without further ado, let's bring forth the videos!</p>
</td>
</tr>
</tbody>
</table>
<h2>A glimpse at the future to whet your appetite&nbsp;</h2>
<p>This first video is full of commentary and stunning examples of robots that lack brains but are capable of learning by way of a design that mimics a central nervous system. &nbsp;Although the video depicts the future as something to be worried about, it's still a great, non technical introduction to whet your appetite and make way for the other more specific videos that are focused on simulations and applications of machine learning.</p>
<p><iframe src="http://www.youtube.com/embed/D21TF1WeNfM" frameborder="0" width="420" height="315"></iframe></p>
<h2>Watch a simulation of robots that learn to drive</h2>
<p>In this video, we see a software demonstration of animated robots that start with no knowledge of a driving course. &nbsp;Watch what happens as the simulation is processed repeatedly, allowing the robots to learn the course with each generation. &nbsp;Buckle up!</p>
<p><iframe src="http://www.youtube.com/embed/lmPJeKRs8gE" frameborder="0" width="420" height="315"></iframe></p>
<p></p>
<h2>Hey Darwin, what do you think of this?</h2>
<p>This next video is progressively more complex, however, the simulation clearly shows how a collection of bots act as they evolve. &nbsp;The bots &nbsp;can attack each other with the intent of killing and eating each other to sustain life. &nbsp;They also reproduce to make baby bots. &nbsp;Take a look at this awesome example of machine learning..</p>
<p><iframe src="http://www.youtube.com/embed/GvEywP8t12I" frameborder="0" width="560" height="315"></iframe></p>
<h2>Virtual Darwinism take two</h2>
<p>This next video is another representation of virtual evolution by machine learning techniques. &nbsp;Watch block like organisms learn to fight each other over a virtual cube that represents a block of food. &nbsp;</p>
<p><iframe src="http://www.youtube.com/embed/-fUHhu_F4Zs" frameborder="0" width="420" height="315"></iframe></p>
<h2>Machine learning for pattern matching and recognition</h2>
<p>This video shows an application that will render CAPTCHA verification methods obsolete. &nbsp;I have a decent amount of experience working with OCR engines however, this is the most accurate recognition I've ever seen.</p>
<p><iframe src="http://www.youtube.com/embed/ocB8uDYXtt0" frameborder="0" width="420" height="315"></iframe></p>
<h2>Show and tell</h2>
<p>This concludes my article on machine learning examples. &nbsp;I hope you enjoyed the article and that it has peaked your interest in machine learning. &nbsp;Do you have a favorite video or application that demonstrates machine learning, neural networks, prediction algorithms, pattern matching or some other related technology? &nbsp;If so, we'd love to hear about it so please leave a comment with a link.</p>
<p>Thanks for reading,</p>
<p>Buddy James</p>
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buddy@refactorthis.comhttp://refactorthis.net/post/2013/02/19/Machine-Learning-5-examples-of-what-it-is-and-why-you-should-care.aspx#commenthttp://refactorthis.net/post.aspx?id=9c568c97-b026-428e-bb14-2a18d21ca54eTue, 19 Feb 2013 23:16:00 -0300.NETSoftware DesignMachine learningArtificial IntelligenceNeural NetworksBig DataPrediction algorithmsSimulated EvolutionRobotsBragisoftAdminhttp://refactorthis.net/pingback.axdhttp://refactorthis.net/post.aspx?id=9c568c97-b026-428e-bb14-2a18d21ca54e83http://refactorthis.net/trackback.axd?id=9c568c97-b026-428e-bb14-2a18d21ca54ehttp://refactorthis.net/post/2013/02/19/Machine-Learning-5-examples-of-what-it-is-and-why-you-should-care.aspx#commenthttp://refactorthis.net/syndication.axd?post=9c568c97-b026-428e-bb14-2a18d21ca54e